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Chapter 5: Problem 40

A Social Security number is used to identify each resident of the United States uniquely. The number is of the form \(x x x-x x-x x x x,\) where each \(x\) is a digit from 0 to 9 (a) How many Social Security numbers can be formed? (b) What is the probability of correctly guessing the Social Security number of the President of the United States?

Short Answer

Expert verified
There are 1,000,000,000 possible Social Security numbers. The probability of correctly guessing the President's Social Security number is \[ \frac{1}{1,000,000,000} \].

Step by step solution

01

Understand the Structure of the Social Security Number

A Social Security number is in the format: \( x x x-x x-x x x x \), where each \( x \) is a digit from 0 to 9.
02

Calculate the Total Number of Possible Social Security Numbers

Each \( x \) can be any digit from 0 to 9, which gives 10 choices. Since there are 9 digits in the Social Security number, the total number of possible Social Security numbers is \( 10^9 \). Thus, the number of possible Social Security numbers is \[ 10^9 = 1,000,000,000 \].
03

Calculate the Probability of Guessing the President's Social Security Number

The probability of correctly guessing a specific Social Security number is the ratio of one particular number to the total number of possible numbers. So, the probability is \[ \frac{1}{10^9} = \frac{1}{1,000,000,000} \].

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Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Probability
Probability is the measure of the likelihood that an event will occur. In this exercise, we are looking at the probability of guessing a specific Social Security number correctly. The formula for probability is simple: it is the number of successful outcomes divided by the total number of possible outcomes. Since there is one correct Social Security number out of a possible 1,000,000,000 (one billion) numbers, the probability of guessing it correctly is very low. Mathematically, we represent it as \(\frac{1}{10^9}\). This means there is only one chance in a billion to guess it right.
Combinatorics
Combinatorics is a branch of mathematics dealing with combinations of objects. In this problem, each digit of the Social Security number can be any number from 0 to 9. Therefore, for each of the 9 positions in the format \( x x x-x x-x x x x \), there are 10 possible choices. We need to find the total number of different combinations possible by multiplying the number of choices for each position. So, the total number of possible Social Security numbers is computed as \(10^9 = 1,000,000,000\). This approach is fundamental in combinatorics, where we multiply the number of choices in each step.
Number Theory
Number theory is a branch of pure mathematics devoted to the study of integers and integer-valued functions. In this exercise, we handle large numbers and operations related to them. For example, counting the total number of Social Security numbers involves using the power of 10, specifically \(10^9\). This falls under number theory as it deals with properties and structures of numbers on a large scale. Number theory provides the foundation for understanding how and why we use these large numerical results in specific contexts, such as unique identifiers.
Mathematical Problem-Solving
Mathematical problem-solving involves a series of logical steps to find a solution to a given problem. In this case, we started by understanding the format of the Social Security number. Each step followed logically from the previous one. We then calculated the total number of possible combinations: \(10^9\). Following this, we determined the probability of guessing the correct number by considering the ratio of one correct Social Security number to the total possible combinations. This structured approach is key in solving mathematical problems, ensuring that each solution is both logical and accurate.
Breaking the problem down into manageable steps helps to understand and solve it efficiently.

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Most popular questions from this chapter

Suppose that \(E\) and \(F\) are two events and that \(P(E)=0.4\) and \(P(F | E)=0.6 .\) What is \(P(E \text { and } F) ?\)

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